# IMG_PATH = "../input/animals-data/dataset/"
# IMG_DATA_PATH = "../data/small_data_oil_for_classification/images/"
# TRAIN_DATA_PATH = "../data/small_data_oil_for_classification/train/"
# TEST_DATA_PATH = "../data/small_data_oil_for_classification/test/"
IMG_DATA_PATH = "../data/data_oil_for_classification/images/"
TRAIN_DATA_PATH = "../data/data_oil_for_classification/train/"
TEST_DATA_PATH = "../data/data_oil_for_classification/test/"
# IMG_DATA_PATH = "../data/oil_image_aug/train/"
# TRAIN_DATA_PATH = "../data/oil_image_aug/train/"
# TEST_DATA_PATH = "../data/oil_image_aug/test/"
# IMG_DATA_PATH = "../data/CarbonateImagesAug/STAT_FULL_train_aug/"
# TRAIN_DATA_PATH = "../data/CarbonateImagesAug/STAT_FULL_train_aug/"
# TEST_DATA_PATH = "../data/CarbonateImagesAug/STAT_FULL_test_aug/"
# DATASET_NAME = "CarbonateImagesAug_STAT"
DATASET_NAME = "data_oil_for_classification-8-2"
NUM_CLASSES = 6  # 6 :small oil  10 cifar 10 tiny_imagenet 200 | oil_aug: 7
IMG_HEIGHT = 224 # 250  # The images are already resized here
IMG_WIDTH = 224 # 250  # The images are already resized here

SEED = 42
TRAIN_RATIO = 0.75
VAL_RATIO = 1 - TRAIN_RATIO
SHUFFLE_BUFFER_SIZE = 100

LEARNING_RATE = 1e-5
EPOCHS = 10
TRAIN_BATCH_SIZE = 4  # Let's see, I don't have GPU, Google Colab is best hope
TEST_BATCH_SIZE = 4  # Let's see, I don't have GPU, Google Colab is best hope
FULL_BATCH_SIZE = 4

ENC_LOSS_WEIGHT = 100    # 100
DEC_LOSS_WEIGHT = 5    # 5
CLS_LOSS_WEIGHT = 100  # 100

######################################
# resizer模块相关参数
in_channels= 1       # Number of input channels of resizer (for RGB images it is 3)
out_channels= 1      # Number of output channels of resizer (for RGB images it is 3)
num_kernels = 16      # Same as `n` in paper
num_resblocks = 2     # Same as 'r' in paper
negative_slope = 0.2  # Used by leaky relu
interpolate_mode= "bilinear"  # Passed to torch.nn.functional.interpolate
image_size = 224 # 250
resizer_image_size =  224 # 224
###### Train and Test time #########

DATA_PATH = "../data/data_oil_for_classification/images/all/"
AUTOENCODER_MODEL_PATH = "baseline_autoencoder.pt"
ENCODER_MODEL_PATH = "../models/conv_encoder_decoder_cls/{}/conv_encoder2_lr{}_epoch{}.pt".format(DATASET_NAME,str(LEARNING_RATE),EPOCHS)
DECODER_MODEL_PATH = "../models/conv_encoder_decoder_cls/{}/conv_decoder2_lr{}_epoch{}.pt".format(DATASET_NAME,str(LEARNING_RATE),EPOCHS)
# CLS_MODEL_PATH = "../models/conv_encoder_decoder_cls/{}/conv_cls_lr{}_epoch{}.pt".format(DATASET_NAME,str(LEARNING_RATE),EPOCHS)
EMBEDDING_PATH = "../models/conv_encoder_decoder_cls/{}/data_embedding_f.npy".format(DATASET_NAME)
EMBEDDING_SHAPE = (1, 256)  # encoder： (1, 256) | encoder2： (1, 512)
FEATURE_DIM = 256 # encoder： 256 | encoder2： 512
MODEL_DIR_PATH = "../models/conv_encoder_decoder_cls/{}/".format(DATASET_NAME)
# TEST_RATIO = 0.2

###### Test time #########
NUM_IMAGES = 10
# TEST_IMAGE_PATH = "../data/small_data_oil_for_classification/test/liefeng/0014.png"
# TEST_IMAGE_PATH = "F:\\DataSet\\tiny-imagenet-200\\test\\n01443537\\n01443537_0.JPEG"

TEST_IMAGE_PATH = "../data/small_data_oil_for_classification/test/liefeng/0003.png"
# TEST_IMAGE_PATH = "../data/small_data_oil_for_classification/test/liefeng/0794.png"
# TEST_IMAGE_PATH = "../data/small_data_oil_for_classification/train/liefeng/0525.png"
TEST_IMAGE_PATH = "../data/data_oil_for_classification/test/wenceng/2024.png"
# TEST_IMAGE_PATH = "../data/data_oil_for_classification/test/lishi/3124.png"
# TEST_IMAGE_PATH = "../data/data_oil_for_classification/test/youdaofeng/5141.png"
# TEST_IMAGE_PATH = "../data/data_oil_for_classification/test/rongkong/1080.png"
# TEST_IMAGE_PATH = "../data/small_data_oil_for_classification/test/ansetiaodai/4391.png"
# TEST_IMAGE_PATH = "F:\\DataSet\\tiny-imagenet-200\\test\\n01443537\\n01443537_0.JPEG"